Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy.

ABSTRACT: Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination techniques. Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herein, this work takes the discrimination of three brands of dairy products as an example to investigate the Raman spectral feature based on the support vector machines (SVM), extreme learning machines (ELM) and convolutional neural network (CNN) algorithms. The results show that there are certain differences in the optimal spectral feature interval corresponding to different machine learni

SUBMITTER: Li JX 

PROVIDER: S-EPMC11208939 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature